Automated (AutomaTED)

To facilitate large-scale, secure data analysis, NIoT is developing AutomaTED, an automated system that anonymizes MAT-held data and makes it available to researchers through OpenSAFELY Schools. This initiative, funded by XTX Markets and the Nuffield Foundation, ensures that sensitive educational data is handled with the highest level of security while allowing researchers to examine trends in teacher development and impact.

AutomaTED is modeled on OpenSAFELY, a secure analysis system originally developed for NHS records. This partnership with Oxford University’s Bennett Institute ensures that education researchers can access meaningful insights while maintaining data privacy. The system will enable:

• Automatic anonymization of school and teacher data, making large-scale research on teacher impact possible without compromising security.
  
• A safe research environment, where analysts can submit research queries and receive aggregated, non-identifiable answers.
  
• Future scalability, allowing more schools and MATs to contribute data, increasing the depth and accuracy of insights into teacher effectiveness.

NioT’s Shaun Dillon and Mark Osborne lead this project, working to ensure that the TED becomes a central resource for evidence-based teacher development.